Conference proceeding
LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS
2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), pp.2012-2016
01 Jan 2018
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Information about an image's source camera model is important knowledge in many forensic investigations. In this paper we propose a system that compares two image patches to determine if they were captured by the same camera model. To do this, we first train a CNN based feature extractor to output generic, high level features which encode information about the source camera model of an image patch. Then, we learn a similarity measure that maps pairs of these features to a score indicating whether the two image patches were captured by the same or different camera models. We show that our proposed system accurately determines if two patches were captured by the same or different camera models, even when the camera models are unknown to the investigator. We also demonstrate the utility of this approach for image splicing detection and localization.
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Details
- Title
- LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS
- Creators
- Owen Mayer - Drexel UniversityMatthew C. Stamm - Drexel UniversityIEEE
- Publication Details
- 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), pp.2012-2016
- Conference
- 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
- Publisher
- IEEE
- Number of pages
- 5
- Grant note
- 1553610 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Identifiers
- 991019170380604721
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InCites Highlights
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- Web of Science research areas
- Acoustics
- Engineering, Electrical & Electronic